Planning Online Advertising Using Gini Indices

81 Pages Posted: 13 Dec 2015 Last revised: 26 Oct 2018

See all articles by Miguel Lejeune

Miguel Lejeune

George Washington University

John Turner

University of California, Irvine - Paul Merage School of Business

Date Written: October 24, 2018

Abstract

We study an online display advertising planning problem in which advertisers’ demands for ad exposures (impressions) of various types compete for slices of shared resources, and advertisers prefer to receive impressions that are evenly-spread across the audience segments they target. We use the Gini coefficient measure and formulate an optimization problem that maximizes spreading of impressions across targeted audience segments while limiting demand shortfalls. First, we show how Gini-based metrics can be used to measure spreading that publishers of online advertising care about, and how Lorenz curves can be used to visualize Gini-based spread so that managers can effectively monitor the performance of a publisher’s ad delivery system. Second, we adapt an existing ad planning model to measure Gini-based spread across audience segments, and compare and contrast our model to this baseline with respect to key properties and the structure of the solutions they produce. Third, we introduce a novel optimization-based decomposition scheme to efficiently solve our Gini-based problem up to 60 times faster than solving a basic formulation directly. Finally, we present a number of model and algorithmic extensions, including (1) an online algorithm which mirrors the structure of our decomposition method to serve well-spread ads in real-time, (2) a model extension which allows an aggregator buying impressions in an external market to allocate them to advertisers in a well-spread manner, and (3) a multi-period model and decomposition method which spreads impressions across both audience segments and time.

Keywords: Online Advertising, Gini Index, Lorenz Curve, Decomposition Method

JEL Classification: C61,M37

Suggested Citation

Lejeune, Miguel and Turner, John, Planning Online Advertising Using Gini Indices (October 24, 2018). Available at SSRN: https://ssrn.com/abstract=2702590 or http://dx.doi.org/10.2139/ssrn.2702590

Miguel Lejeune

George Washington University ( email )

Washington, DC 20052
United States

John Turner (Contact Author)

University of California, Irvine - Paul Merage School of Business ( email )

Paul Merage School of Business
Irvine, CA California 92697-3125
United States

HOME PAGE: http://faculty.sites.uci.edu/turnerjg/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
328
Abstract Views
1,721
Rank
181,032
PlumX Metrics